Modeling gamma radiation exposure rates using geologic and remote sensing data to locate radiogenic anomalies.

Abstract

Aerial Gamma-Ray Surveys (GRS) are ideal for tracking anthropogenic gamma radiation releases and transport. The interpretation of a GRS can be complicated by natural gamma-ray sources such as atmospheric radon, cosmic rays, geologic materials, and even the survey equipment itself. Some of these complicating factors can be accounted for or corrected by calibration or mathematic techniques. Real-time algorithms that attempt to enhance potential radiogenic anomalies over background are also in use. However, natural geology is a source of significant background gamma-ray production and neither mathematical corrections nor real-time algorithmic approaches directly account for geology and geochemistry. In this study, we advance techniques to predict geologic background exposure rates using rapid and practical methods which can be achieved in the field. In addition we generate models that focus specifically on highlighting radiogenic anomalies for emergency response or further investigation. Predictive models developed in this study were generally able to predict background with medians of ± 1.0\u202fμR/h compared to measured data, and were also able to highlight anomalous areas even where radiation exposure rates were within the range of natural background.